Method, system, and storage medium for providing a dynamic, multi-dimensional commodity modeling process

ABSTRACT

An exemplary embodiment of the invention relates to a method, system, and storage medium for providing a dynamic multi-dimensional commodity modeling process. The system includes a data collection component operable for collecting raw data, a dynamic multi-dimensional commodity model component, and a commodity tree generated by the dynamic multi-dimensional commodity model component. The system also includes a closed loop/corrective action component operable for resolving nonconformance issues resulting from analysis of the raw data and commodity tree, and an analytic engine in communication with the data collection component, the multi-dimensional commodity model component, and the closed loop/corrective action component. The analytic engine receives the raw data from the data collection component, receives the commodity tree, performs analytics on the raw data according to rules defined by the commodity tree, and transmits any nonconformance data resulting from performing the analytics to the closed loop/correction action component for resolution of any identified nonconformances.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. Ser. No.11/840,531 filed Aug. 17, 2007, which is a divisional application ofU.S. Ser. No. 10/652,017 filed Aug. 29, 2003, the contents of bothapplications being incorporated by reference herein in their entirety.

BACKGROUND

The present invention relates generally to quality management systems,and more particularly, the invention relates to a method, system, andstorage medium for providing a dynamic multi-dimensional commoditymodeling process for implementation via a quality management system.

Manufacturing operations typically involve some degree of monitoringproduction quality performance and provide quality control capability bymonitoring and analyzing quality data. While various softwareapplications exist for facilitating these activities, they are generallylimited to a fixed set of programs that analyze and monitor quality dataacross static product characteristics. For example, in a hardwaremanufacturing environment, such technology may provide the capability ofmonitoring power supplies, specific suppliers, a unique part number,etc. Any desire to change the analytics or product dimensions to bemonitored results in the need for extensive hard-code changes to thecomputing application or query. Attempts to rewrite software queriesthat will measure atypical characteristics take time, such as a fewhours to several days. Thus, the ability to change performancemonitoring and control actions across multiple part dimensions orcharacteristics in near real-time is not feasible.

This problem becomes significant in large manufacturing operations wherethousands, or tens of thousands, of component parts are utilized forproduction, especially when many of these parts have commoncharacteristics (e.g., same supplier, same function, same size, etc.).In these operations, there exists a near daily need to analyze qualitydata in a variety of ways to understand part performance issues. Thedynamic need to change analytics across multiple dimensions presentssignificant problems with existing processes and technology. Thisproblem is most evident in manufacturing operations where complexproducts are produced in a “build-to-order” environment with a highdegree of featurability. As operations move from mass production of likeproducts to customized assemblies with a lot size of one, simpleanalytics such as failure rates are ineffective in characterizingperformance.

What is needed, therefore, is a way to provide flexible, commodity datamodeling that allows for analysis criteria to be alterable in a nearreal-time environment.

SUMMARY

An exemplary embodiment of the invention relates to a method, system,and storage medium for providing a dynamic multi-dimensional commoditymodeling process. The system includes a data collection componentoperable for collecting raw data, a dynamic multi-dimensional commoditymodel component, and a commodity tree generated by the dynamicmulti-dimensional commodity model component. The system also includes aclosed loop/corrective action component operable for resolvingnonconformance issues resulting from analysis of the raw data andcommodity tree, and an analytic engine in communication with the datacollection component, the multi-dimensional commodity model component,and the closed loop/corrective action component. The analytic enginereceives the raw data from the data collection component, receives thecommodity tree, performs analytics on the raw data according to rulesdefined by the commodity tree, and transmits any nonconformance dataresulting from performing the analytics to the closed loop/correctionaction component for resolution of any identified nonconformances.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings wherein like elements are numbered alikein the several FIGURES:

FIG. 1 is a block diagram illustrating a quality management system uponwhich the multi-dimensional commodity modeling process is implemented inan exemplary embodiment;

FIG. 2 is a diagram of the features of the multi-dimensional commoditymodel component of a quality management system in an exemplaryembodiment;

FIG. 3 is a flowchart describing a two-phase process of themulti-dimensional commodity model component for building a commoditytree structure in an exemplary embodiment;

FIG. 4 is a diagram of a level 1 commodity constituent model that isassociated with the CCM level 1 phase in an exemplary embodiment;

FIG. 5 is a diagram of a level 2 commodity constituent model that isassociated with the CCM level 2 phase in an exemplary embodiment; and

FIG. 6 is a diagram of a sample commodity tree created by themulti-dimensional commodity modeling component in an exemplaryembodiment.

DETAILED DESCRIPTION

Embodiments of the invention include a multi-dimensional commodity modelprocess, which as implemented, facilitates an on-demand qualitymonitoring and control process that is tailorable to meet the needs ofany quality management system as well as for use in any industry. Bydeveloping a unique data modeling approach, this process provides thecapability to automatically monitor performance on an unlimited numberof elements, across any number of dimensions, utilizing multipleanalytic algorithms without changes in program hard-coding. Thismodeling approach also provides the ability to change the time historyof the data being analyzed, providing additional flexibility inperformance monitoring across the dimension of time. Integration of thisdata modeling approach into an existing quality management systemproduces a dynamic ability to facilitate real-time quality performancemonitoring and control. While embodiments of the invention are describedherein with respect to an electronics manufacturing industry, it will beunderstood that the multi-dimensional commodity model may be applied toany industry that performs quality management functions. Thus, theexamples provided with respect to the electronics industry are intendedfor purposes of illustration and are riot to be construed as limiting inscope.

The following definitions are supplied in reference to the components ofthe multi-dimensional commodity model.

Commodity. As used herein, a commodity refers to a high-level (e.g., toplevel) grouping of elements that are arranged according to relationshipfactors. For example, in the electronics industry, commodities mightinclude power supplies, mechanical parts, cables, electronics anddecorative covers.

Sub-commodity. A sub-commodity refers to a more granular grouping of acommodity used to detail the sub-elements of a commodity. Asub-commodity can have multiple sub-commodities identified within it(i.e., nested sub-commodities) to define things such as classification,reliability, physical characteristics, naming convention, supplier ormarketing preferences or any other suitable attributes. An example ofsub-commodities for an electronics commodity might be memory,processors, and input/output devices.

Constituent. A constituent refers to the detailed component or basicfoundational unit or element of a commodity from which measurements areperformed. For example, under the sub-commodity processors, a specificconstituent might be a part number ‘99X9999’.

Node. A node refers to any level of grouping of commodities,sub-commodities, or constituents in a commodity tree for the purpose ofperforming an analysis.

A commodity tree refers to a hierarchical data structure for a commodityincluding associated sub-commodities and constituents and is created bythe multi-dimensional commodity model process. Commodity trees comprisea top level node(s), optional secondary level nodes (e.g.,sub-commodities and nested subcommodities), and leaf level nodes (e.g.,constituent nodes). The nodes of the commodity tree are assigned uniformattributes and dimensional attributes. These attributes are theninherited down the commodity tree to all applicable dependent nodes.

Commodity constituent model Level 1 (CCM L1). A set of attributes thatdefines the uniform characteristics of interest associated with nodesfor a particular commodity and for which analytics are to be performed.

Commodity constituent model Level 2 (CCM L2). A set of attributes thatdefines the dimensions to analyze, along with the associated trends andpatterns of interest, for any particular node in any commodity tree.

FIG. 1 is a block diagram describing the multi-dimensional commoditymodel in the context of a quality management system (QMS) environment.It will be understood by those skilled in the art, however, that themulti-dimensional commodity model may be implemented in any environmentthat desires to analyze large amounts of data in a variety of ways;thus, the on-demand QMS 100 of FIG. 1 is described herein forillustration and is not intended to be limiting in scope. The QMS 100may comprise a commercial quality management application or may be IBM'sProduct Quality Management System™. Further, while the invention isdescribed with respect to a specific manufacturing industry, i.e.,electronics, it will be understood that the modeling features of themulti-dimensional commodity model may be applied to any industry thatmay benefit from utilizing a quality management system. The fourcomponents of QMS 100 comprise: data collection 102, multi-dimensionalcommodity model 104, analytic engine 106, and closed loop/correctiveaction 108.

Data collection component 102 provides performance and parametric datato analytic engine 106 by collecting all relevant data spanning a rangeof activities from the procurement of raw materials to customerinstallation and return of products and components. The processperformed by data collection component 102 is highly dependent on theproduct characteristics from commodity to commodity. For example, in anelectronics manufacturing environment, high-cost storage subsystems mayrequire more detail and component information than a label that isvalued under a dollar. Additionally the performance data for a suppliersourced assembly may include the details of the assembly or could be a‘pass/fail’ of the assembly itself.

Multi-dimensional commodity model component 104 provides an efficientmethod to dynamically select what data (e.g., commodities,sub-commodities, constituents) to analyze, how to analyze it, and whatpatterns and trends to look for. These models are created bymulti-dimensional commodity model component 104 and act as a virtual setof controls that allow QMS analytics to be modeled in multiple ways. Theinheritance properties of these models, along with their two-leveldesign, provide virtually unlimited capability to analyze a variety ofattributes and dimensions, to change these attributes and dimensions atwill, while maintaining uniform characteristics for each commodity. Thereliance on hard-coded queries and applications known in the art andexisting in current solutions can be eliminated. The process performedby the multi-dimensional commodity model 104 results in a set ofcommodity constituent models and is described further herein.

Analytic engine 106 applies the commodity constituent models created bymulti-dimensional commodity model 104. It also prunes the data asdefined by the commodity constituent models and applies analytics to thepruned data as defined by the commodity constituent models. Analyticengine 106 also runs secondary analysis on the analytic output toidentify nonconforming trends and patterns as defined by the commodityconstituent models and automatically notifies all nonconforming trendsto the closed loop/corrective action process 108.

The process 110 of analytic engine 106 represents an instantiation of aset of commodity constituent models from 104. Process 112 utilizes thecommodity constituent model attributes instantiated in process 110 toprune the data from data collection component 102. The output of process112 is the pruned data of constituents identified by the commodityconstituent models having attributes and dimensions that meet thecriteria provided in the model. The output of process 112 is used byprocess 114. Process 114 performs the analysis defined by the commodityconstituent models on the pruned data. The output of process 114 is aset of analytics for the dimensions specified in the commodityconstituent models. Process 116 takes the analytics output by process114 and performs a secondary analysis that looks for patterns andtrends, which are also defined by the commodity constituent model,identifying those constituents that match the patterns and trends(referred to as nonconformances). Process 116 then automaticallygenerates a message or alert to the closed/loop corrective actioncomponent 108 identifying the nonconformances so that action can betaken to alleviate future nonconformances.

Closed loop/corrective action process 108 drives problems, issues, ornonconformance items to closure. While the multi-dimensional commoditymodel component 104 defines the dimensions to be analyzed and the trendsand patterns to search for, the process described in closedloop/corrective action component 108 is the “action end” of the systemwhereby identified nonconformances are assigned to owners and apragmatic, closed-loop process is employed to ensure the appropriatecorrective actions have been taken for each nonconformance.

Multi-dimensional commodity model 104 comprises a commodity hierarchydata structure and level 1 attributes referred to as commodityconstituent model level 1 (CCM L1) 202 as shown in FIG. 2. As describedabove, CCM L1 202 refers to a set of attributes that defines the uniformcharacteristics of interest associated with a particular commodity forall analytics to be performed. Multi-dimensional commodity model 104also comprises a number of dimensions and level 2 dimensional attributesreferred to as commodity constituent model level 2 (CCM L2) 204. Asdescribed above CCM L2 204 refers to a set of attributes, that definesthe dimensions to analyze, along with the associated trends and patternsof interest, for any particular element in any particular commoditymodel. These attributes and dimensions are dynamically alterable via themulti-dimensional commodity model component 104 during instantiation ofthe analytic process. Commodity tree 206 combines the elements providedin CCM L1 202 and CCM L2 204. A sample commodity tree is shown ingreater detail in FIG. 6. Once created, commodity tree 206 is ready tobe processed by analytic engine 106 as described above in FIG. 1.

A commodity tree is created by the multi-dimensional commodity model 104utilizing a two-phase process as described in FIG. 3. The first phase ofthe process is to establish the base commodity hierarchy which definesthe relationships between commodities, sub-commodities (as well asnested sub-commodities, if desired), and the constituents that areassigned to those sub-commodities. This process builds a set of treedata structures for assigning the CCM L1 attributes and CCM L2dimensional attributes. An individual accesses the multi-dimensionalcommodity model 104 of QMS 100 at step 302. Initial assignment ofcommodities and sub-commodities to constituents is performed at step304. All attributes that provide uniform characteristics during the CCML1 phase to each commodity tree are established at step 306. Attributescan be user-defined according to the type of quality management systemused and the nature of the industry utilizing the multi-dimensionalcommodity model 104. The attributes defined for a manufacturing industrymay include sampling criteria, period definition, history definition,type of measure/analytic or any other attribute desired. At step 308, itis determined whether additional attributes are to be defined. If thereare additional attributes to be defined, the process reverts back tostep 304. If, on the other hand, all the attributes have been assigned,the second phase begins.

The second phase establishes dimensions for analyzing each commodity. Atstep 310, the commodity tree created by steps 302-308 is examined todetermine which nodes or level in the commodity tree are to be analyzed.At step 312, it is determined what patterns or trends at the nodes/levelwithin the tree are to be analyzed. These trends or patterns may includeelements such as performance tolerances, noise filters, oscillationthresholds or trends, consecutive trending, and negative performancethreshold. At step 314, it is determined whether there are additionalnodes or levels to be analyzed. If there are additional nodes or levelsto be analyzed, the process reverts to step 310. Once all of theattributes have been established for each commodity, the resultingcommodity tree is created at step 316 and may be immediately utilized byanalytic engine 106 to search, analyze and indicate the results of theintended mapping at step 318. The model is instantiated for eachexecution of analytic engine 106, and thus the controls and dials of thecommodity constituent model can be changed virtually in real-time,anytime, and subsequent cycles will run with each new modelinstantiation.

In an alternative embodiment, a user may access the multi-dimensionalcommodity model 104 at step 320 and bypass the CCM L1 phase definitionsif desired. This may be desirable where attributes for CCM L1 havealready been established and it is not necessary to access thesefeatures.

FIG. 4 illustrates a level 1 commodity constituent model 202 produced bythe multi-dimensional commodity model 104. The modeling of the commodityhierarchy and CCM L1 attributes build the base commodity hierarchy foreach commodity and provides uniform characteristics to each commoditytree. This tree can be created, changed, and updated in real time and isthe base infrastructure for all analysis for the analytic engine. Theattributes assigned during this phase are only used when CCM L2selection is made on a particular node in the tree.

It is important to note that all analytics are derived from the detailedconstituents where the actual performance occurs. This process builds aset of data structures that represents several n-level tertiary treeswhere the top node in each tree represents the overall commodity 402 andthe leaf nodes (also referred to as constituents) 406 end up as thedetailed constituents to be analyzed as demonstrated in FIG. 4.

Commodity 402 may have sub-commodities 404 and nested sub-commodities405 that are 0 . . . n levels deep to provide granular sub-groupings.These nodes are only analyzed when CCM L2 selections are made at theseparticular levels within the commodity tree. The number ofsub-commodities 404 and nested sub-commodities 405 may be user-defined.However, every constituent 406 is directly assigned to one and only onedependent node within a commodity tree. Other commodity trees can usethe same constituents but not within the same tree. CCM L1 criteria isassigned at the commodity level and inherited to every node in the treein order to ensure that all nodes have all uniform characteristicsassigned.

Examples of uniform attributes 408 may include sampling criteria, perioddefinition, history definition, and type of measure/analytic. Samplingcriteria defines what data to sample such as product types, operations,steps, sources, etc. Period definition defines the unit of time to applythe specified analytic such as hour, day, week, or month. Historydefinition defines the number of periods to be applied to the specifiedanalytic. Type of measure/analytic defines the type of analytic to beapplied such as standard Shewhart Control Charts (i.e., p-chart,np-chart, u-chart, or other similar charts.

FIG. 5 illustrates a level 2 commodity constituent model 204. Themodeling of the CCM L2 attributes establishes what dimensions are to beanalyzed for each commodity. This process includes examining the entirecommodity tree and selecting which nodes (e.g., level in the tree ormodel) and what patterns or trends at that level within the tree will beassigned dimensional attributes for analysis. The level 2 processupdates tertiary trees created in the first part of the CCM process(Level 1) 202 with the dimensions and trends specified for each nodewithin the tree as demonstrated in FIG. 5. CCM Level 2 is applied to thecommodity tree by assigning “none”, “any”, or “all” nodes in the tree ateach node level. These assignments are inherited down the tree to everydependent node in the tree that has a CCM L2. All leaf nodes (i.e.,constituents) 406 inherit all CCM level 2 criteria regardless of whetherthe leaf node has a CCM level 2 assigned or not. Only nodes that resultin a CCM level 2 assigned, either directly or inherited, will beanalyzed.

Typical monitoring dimensions such as dimensions 502-508 used by themulti-dimensional commodity model 104 may include performancetolerances, noise filters, oscillation thresholds or trends, consecutivetrending, negative performance threshold, and any other dimensionsdesired. Assignment of dimensions 502-508 to the commodity structuredefines what will be analyzed (e.g., node) and establishes inheritanceattributes. Performance tolerances define standard deviations from themean. Noise filters define what is statistically significant sample sizeper period. Oscillation thresholds or trends define unwanted changeoscillating around the mean within limits. Consecutive trending definessignificant trending (negative or positive). Negative performancethreshold defines absolute value limits regardless of sample size ortrend.

FIG. 6 illustrates a sample commodity tree 206 produced by themulti-dimensional commodity model and describes its dimensions andinheritance properties. The sample commodity tree 206 demonstrates howdimensions are assigned and inherited and result in analyzed dimensionsof a commodity tree. In the commodity tree 206 of FIG. 6, the commodityconstituent mapping is a simple tree consisting of two sub-commoditieswith three constituents categorized or related to sub-commodity 1 andone constituent related to sub-commodity 2. The quality engineer orother professional has assigned four specific dimensions to be analyzeddirectly. This includes all the attributes assigned by the level 2attributes. The four dimensions 502-508 were assigned at differentlevels within the commodity tree as shown in FIG. 5. Dimension A 502 wasassigned to the entire Commodity 1. Dimension B 504 was assigned to theentire sub-commodity 2, dimension C 506 was assigned for the specificconstituent 1, and dimension D 508 was assigned for the specificconstituent 2.

The inheritance policy of the commodity constituent model L2 results inthe following:

Since dimension A 502 was assigned at the highest level—the commodity,every node in the tree that is either identified to be analyzed byanother assignment of a dimension or every constituent under commodity 1inherits dimension A 502 attributes. In this example, this means thatsince sub-commodity 1 is not specified as a dimension to be analyzed, itis skipped and no inheritance occurred. On the contrary, sincesub-commodity 2 has been identified as an analyzed dimension (byassignment of dimension B 504), sub-commodity 2 also inherits dimensionA 502 which implies sub-commodity 2 will be analyzed with dimension A502 and B 504 attributes. The secondary effect of assigning dimensionsat the commodity level is that all root nodes (i.e., constituents 1-4)are automatically assigned dimension A 502 to be analyzed. In thisexample, the assignment of dimension A 502 resulted in five inheriteddimensions to sub-commodity 2 and constituents 1-4 406. Since dimensionB 504 was assigned for sub-commodity 2, not only is sub-commodity 2analyzed to this dimension, but also inherited to constituent 4. Thusconstituent 4 now is assigned both dimension A 502 and B 504 to beanalyzed. Dimension C 506 was assigned to constituent 1 and dimension D508 was assigned to constituent 2 which are the lowest levels in thecommodity model. All dimensions assigned at the constituent level arenot inherited by any other nodes and results in a pure analysis assignedonly to this constituent in this commodity tree. The net impact of theassignment of the four dimensions (A, B, C, and D) resulted in a totalof 9 dimensions analyzed where six of the dimensions are a result of theinheritance policy.

Although all dimensions are assigned at different levels within thecommodity tree, it is important to note that all dimensions are derivedfrom the detailed constituents. Since the CCM L1 attributes establisheduniform characteristics across all nodes under the commodity, theseanalyses and analytics are derived from the bottom up from the nodesthat inherited its properties as illustrated in FIG. 6.

The data modeling approach described above delivers multi-dimensionalflexibility in quality data analysis without the need for extensiveprogram hard-coding or rewrites. This invention is readily integratedinto existing quality management systems. As a result, this inventionbecomes an enabler for dynamic queries of quality data in nearreal-time. As business operations and products change over time, thisdata modeling approach facilitates rapid changes in quality performancemonitoring activities. The quality control and monitoring processenabled by this invention delivers the platform for time relevantquality data analysis, analysis of patterns and trends, predictiveindicators, and uncovering the quality ‘needle in the haystack’.

As described above, the present invention can be embodied in the form ofcomputer-implemented processes and apparatuses for practicing thoseprocesses. The present invention can also be embodied in the form ofcomputer program code containing instructions embodied in tangiblemedia, such as floppy diskettes, CD-ROMs, hard drives, or any othercomputer-readable storage medium, wherein, when the computer programcode is loaded into and executed by a computer, the computer becomes anapparatus for practicing the invention. The present invention can alsobe embodied in the form of computer program code, for example, whetherstored in a storage medium, loaded into and/or executed by a computer,or transmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via electromagneticradiation, wherein, when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingthe invention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

While preferred embodiments have been shown and described, variousmodifications and substitutions may be made thereto without departingfrom the spirit and scope of the invention. Accordingly, it is to beunderstood that the present invention has been described by way ofillustration and not limitation.

1. A quality management system for utilizing dynamic multi-dimensionalcommodity modeling, comprising: a data collection component operable forcollecting raw data; a dynamic multi-dimensional commodity modelcomponent; a commodity tree generated by the dynamic multi-dimensionalcommodity model component; a closed loop/corrective action componentoperable for resolving nonconformance issues resulting from analysis ofthe raw data and commodity tree; and an analytic engine in communicationwith the data collection component, the multi-dimensional commoditymodel component, and the closed loop/corrective action component, theanalytic engine performing; receiving the raw data from the datacollection component; receiving the commodity tree; performing analyticson the raw data according to rules defined by the commodity tree; andtransmitting any nonconformance data resulting from performing theanalytics to the closed loop/correction action component for resolutionof any identified nonconformances.
 2. The quality management system ofclaim 1, wherein generating the commodity tree comprises: creating acommodity hierarchy data structure comprising: at least one top levelnode; and at least one leaf node dependent upon said at least one toplevel node; a commodity constituent model created by assigningattributes to nodes in the hierarchy, the attributes sharing uniformcharacteristics and used in the analytics performed by the analyticengine; and a second commodity constituent model created by selectivelyassigning at least one dimensional attribute to a node in the hierarchythat is used in the analytics performed by the analytic engine; whereindependent nodes inherit dimensional attributes assigned to correspondingupper level nodes.
 3. The quality management system of claim 2, whereinthe dimensional attributes are dynamically alterable duringinstantiation of the analytics engine via the dynamic multi-dimensionalcommodity model component.
 4. The quality management system of claim 2,wherein the at least one dimensional attribute includes at least one of:a performance tolerance; a noise filter; an oscillation threshold ortrend; consecutive trending; and negative performance threshold.
 5. Thequality management system of claim 1, wherein the nonconformances areassigned to owners for corrective action.
 6. The quality managementsystem of claim 1, wherein the analytics engine instantiates thecommodity constituent models, prunes the raw data received from the datacollection component, performs an analysis on the pruned data as definedby the commodity constituent models, the analysis resulting in a set ofanalytics for the at least one dimensional attribute specified by thesecond commodity constituent model, and applying the analytics todetermine the existence of any non-conformances.
 7. The qualitymanagement system of claim 1, wherein the raw data includes at least oneof: procurement of raw materials; customer installation; and return ofproducts and components.